{{Notability, date=May 2010 Information fuzzy networks (IFN) is a greedy machine learning algorithm for supervised learning. The

data structure In computer science, a data structure is a data organization, management, and storage format that is usually chosen for efficient access to data. More precisely, a data structure is a collection of data values, the relationships among them, a ...

produced by the learning algorithm is also called Info Fuzzy Network. IFN construction is quite similar to decision trees' construction. However, IFN constructs a directed graph and not a tree. IFN also uses the

conditional mutual information In probability theory, particularly information theory, the conditional mutual information is, in its most basic form, the expected value of the mutual information of two random variables given the value of a third. Definition For random var ...

metric in order to choose features during the construction stage while decision trees usually use other metrics like entropy or gini.

IFN and the knowledge discovery process's stages

* Discretization of continuous features * Feature selection * Creates a model for

classification Classification is a process related to categorization, the process in which ideas and objects are recognized, differentiated and understood. Classification is the grouping of related facts into classes. It may also refer to: Business, organizat ...

* Evaluation of extracted

association rules Association rule learning is a rule-based machine learning method for discovering interesting relations between variables in large databases. It is intended to identify strong rules discovered in databases using some measures of interestingness.P ...

and prioritizing them * Anomaly detection

Attributes of IFN

# The IFN model partially solves the fragmentation problem that occurs in decision trees (the deeper the node the less records it represent. Hence, the number of records might be too low for

statistical significance In statistical hypothesis testing, a result has statistical significance when it is very unlikely to have occurred given the null hypothesis (simply by chance alone). More precisely, a study's defined significance level, denoted by \alpha, is the p ...

indication) since the entire set of records is used in every layer. # Every node inside the net is called an inner or hidden node. # In IFN every variable can appear in only one layer, and there cannot be more than one attribute in a layer. Not all attributes must be used. # The increase in conditional MI of the target variable after building the net equals to the sum of the increase in conditional MI in all layers. # The arcs from terminal nodes to the target variable nodes are weighted (terminal nodes are nodes directly connected to the target variable nodes). The weight is the

due to the arc. # IFN was compared on few common datasets to the

c4.5 C4.5 is an algorithm used to generate a decision tree developed by Ross Quinlan. C4.5 is an extension of Quinlan's earlier ID3 algorithm. The decision trees generated by C4.5 can be used for classification, and for this reason, C4.5 is often referr ...

decision tree A decision tree is a decision support tool that uses a tree-like model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. It is one way to display an algorithm that only contains condit ...

algorithm. The IFN model usually used less variables and had fewer nodes. The

accuracy Accuracy and precision are two measures of ''observational error''. ''Accuracy'' is how close a given set of measurements (observations or readings) are to their ''true value'', while ''precision'' is how close the measurements are to each other ...

of the IFN was smaller than the one of the decision tree. The IFN model is usually more stable, which means that small changes in the training set will affect it less than in other models.

IFN construction algorithm

Input: a list of input variables that can be used, a list of data records (training set) and a minimal statistical significance used to decide whether to split a node or not (default 0.1%). # Create the root node and the layer of the target variable. # Loop until we have used up all the attributes or it cannot improve the

any more with any

. ## Find the attribute with the maximal

. ## Verify that the contribution of the attribute has statistical significance using the

likelihood ratio test In statistics, the likelihood-ratio test assesses the goodness of fit of two competing statistical models based on the ratio of their likelihoods, specifically one found by maximization over the entire parameter space and another found after im ...

. ## Split any node in the previous layer if the contribution of the current attribute has statistical significance. Otherwise, create a node from that node to one of the value nodes of the target variable, according to the majority rule. # return the list of variables chosen to be used by the net and the net itself.

External links

Fuzzification and Reduction of Information-Theoretic Rule Sets
in Data Mining and Computational Intelligence, A. Kandel, M. Last, and H. Bunke (Eds), Physica-Verlag, Studies in Fuzziness and Soft Computing, Vol. 68, pp. 63–93, 2001.
A Comparative Study Of Artificial Neural Networks And Info Fuzzy Networks On Their Use In Software Testing
Classification algorithms